Machine Learning with R Cookbook - Second Edition

How it works...

In this recipe, we discussed pruning a classification tree to avoid over-fitting and producing a more robust classification model. We first located the record with the minimum cross-validation errors within the cptable, and we then extracted the CP of the record and assigned the value to churn.cp. Next, we used the prune function to prune the classification tree with churn.cp as the parameter. Then, by using the plot function, we graphically displayed the pruned classification tree. From the preceding figure, it is clear that the split of the tree is less than the original classification tree (The graphical display of a classification tree figure). Lastly, we produced a classification table and used the confusion matrix to ...